:** Tutorials on Word2Vec in Python. Learns semantic relationships between words in very large corpora by mapping each word to a high-dimensional word embedding. Semantic relationships are estimated using contextual frequency, i.e. how often a word appears given a context of other words.

Course outline for COMPSCI 4414A/9637A/9114A

From Dan: This is a very high-demand course that interests students in various programs across campus. I think this is great because the diversity of backgrounds assembled in the class makes for a better learning experience for all. (Myself included!) However, space is limited. Because of the volume of requests I receive, I am not able to manage a wait list. Students will have to monitor the registration website for available spots. However, all are welcome to sit in the room if there is space.

Objective

The objective of this course is to introduce students to data science (DS) techniques, with a focus on application to substantive (i.e. "applied") problems. Students will gain experience in identifying which problems can be tackled by DS methods, and learn to identify which speciﬁc DS methods are applicable to a problem at hand. During the course, students will gain an in-depth understanding of a particular (substantive problem, DS solution) pair, and present their ﬁndings to their peers in the class. Although this course does not assume prior machine learning or visualization knowledge, it does require students to show substantial initiative in investigating methods that are applicable for their project. The lectures give an overview of important methods, but the lecture content alone is not sufficient to produce a high quality course project.

Prerequisites

At least one undergraduate programming course (e.g. CS2035) and at least one statistics course (e.g. STAT1024.) This course entails a significant amount of self-directed learning and is directed toward fourth-year undergraduate and graduate students.

Register for a wiki account. You will need to use the wiki to let us all know about data sources you find, indicate which dataset you are using, and slot yourself in for brainstorming. Also, everyone should free to make improvements to any part of the wiki. (E.g. if you find some useful software or other resources.)

Slot yourself in for a brainstorming session in the Timeline portion at the bottom of this page before end of Friday, 6 Oct at 5pm or Dan will pick a slot for you.

Tutorials on Word2Vec in Python. Learns semantic relationships between words in very large corpora by mapping each word to a high-dimensional word embedding. Semantic relationships are estimated using contextual frequency, i.e. how often a word appears given a context of other words.

Evaluation

There will be a midterm test but no final exam. Each student will lead a brainstorming session, produce a proposal, draft, and report for a course project. Graduate students (9637) will additionally submit peer reviews of other class projects. For detailed requirements, see Project Guidelines.

Scholastic offences are taken seriously and students are directed to read the appropriate policy, specifically, the definition of what constitutes a Scholastic Offence, at this website: [2].

Daily Quizzes – 5%

Starting on the second lecture, there will be a very short quiz at the beginning of class covering the previous day's materials. The final quiz will be on 31 Oct. The lowest quiz mark will be dropped. Quiz marks will only be excused for medical reasons.

Midterm - 35%

Assessing competencies from the fundamentals taught in the first half of the class.

Brainstorming Session – 5%

Each student will prepare a presentation explaining an applied problem, as well as some potential data science methods that could be applied to the problem. The presentation should be no more than 10 minutes. We will then discuss the problem as a class, along with possible approaches for solving the problem using data science methods. The student is expected to be prepared to answer deep questions about the nature of their problem to ensure that they receive high quality feedback from the brainstorming session.

Project Proposal – 4414: 15% 9637: 10%

Document detailing the plan for the project. See Project Guidelines for detailed requirements.

Report Draft – 5%

A draft of the final report will be due approximately midway through the term. The purpose of the draft is to allow the instructor to provide feedback on the quality of the writing and the direction of the project.

Project Report – 35%

Each student will prepare a research paper detailing a substantive problem, the data available, the applicable data science methods, and empirical results obtained on the problem.

Peer Review – 9637 only: 5%

Each graduate student will prepare two reviews of their classmates' work.

Participation and Effort

Success of the course as a useful learning experience hinges on active participation and effort of the students. Students are expected to attend all classes and are expected to actively participate in the brainstorming sessions.

Accessibility and Support Available at Western

Please contact the course instructor if you require lecture or printed material in an alternate format or if any other arrangements can make this course more accessible to you. You may also wish to contact Services for Students with Disabilities (SSD) at 661-2111 ext. 82147 if you have questions regarding accommodation.
Support Services
Learning-skills counsellors at the Student Development Centre (http://www.sdc.uwo.ca) are ready to help you improve your learning skills. They offer presentations on strategies for improving time management, multiple-choice exam preparation/writing, textbook reading, and more. Individual support is offered throughout the Fall/Winter terms in the drop-in Learning Help Centre, and year-round through individual counselling.
Students who are in emotional/mental distress should refer to Mental Health@Western (http://www.health.uwo.ca/mental_health) for a complete list of options about how to obtain help.
Additional student-run support services are offered by the USC, http://westernusc.ca/services.
The website for Registrarial Services is http://www.registrar.uwo.ca.